Ran Gao

Ran Gao

16 packages on CRAN

BinNonNor

cran
99.99th

Percentile

Generation of multiple binary and continuous non-normal variables simultaneously given the marginal characteristics and association structure based on the methodology proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

BinNor

cran
99.99th

Percentile

Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.

99.99th

Percentile

Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

BivUnifBin

cran
99.99th

Percentile

Simulation of bivariate uniform data with a full range of correlations based on two beta densities and computation of the tetrachoric correlation (correlation of bivariate uniform data) from the phi coefficient (correlation of bivariate binary data) and vice versa.

99.99th

Percentile

Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.

MultiOrd

cran
99.99th

Percentile

A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).

MultiRNG

cran
99.99th

Percentile

Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

OrdNor

cran
99.99th

Percentile

Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with pre-specified correlation matrix and marginal distributions.

99.99th

Percentile

Generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.

PoisBinOrd

cran
99.99th

Percentile

Generation of multiple count, binary and ordinal variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.

99.99th

Percentile

Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties.

99.99th

Percentile

Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.

PoisNonNor

cran
99.99th

Percentile

Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.

PoisNor

cran
99.99th

Percentile

Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.

UnivRNG

cran
99.99th

Percentile

Pseudo-random number generation of 17 univariate distributions.

RNGforGPD

cran
99.99th

Percentile

Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017), and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012).